Systems and methods for verifying whether vehicle operators are paying attention

ABSTRACT

Systems and methods for verifying whether vehicle operators are paying attention are disclosed. Exemplary implementations may: generate output signals conveying information related to a first vehicle operator; make a first type of determination of at least one of an object on which attention of the first vehicle operator is focused and/or a direction in which attention of the first vehicle operator is focused; make a second type of determination regarding fatigue of the first vehicle operator; make a third type of determination of at least one of a distraction level of the first vehicle operator and/or a fatigue level of the first vehicle operator; and effectuate a notification regarding the third type of determination to at least one of the first vehicle operator and/or a remote computing server.

FIELD OF THE DISCLOSURE

The present disclosure relates to systems and methods for verifyingwhether vehicle operators are paying attention.

BACKGROUND

In-vehicle technology to detect, after a vehicle event has occurred,whether a driver was paying attention, is known. For example, a videorecording of the cabin may be analyzed by a human reviewer to revealwhat the driver was doing just before an accident happened. In-vehicletechnology to determine whether a driver is prepared to assume controlof a vehicle that is being operated by an autonomous driving algorithmmay be known.

SUMMARY

One aspect of the present disclosure relates to a system configured forverifying whether vehicle operators are paying attention duringoperation of a first vehicle. The system may include a set of sensorsand one or more hardware processors configured by machine-readableinstructions. The set of sensors may be configured to generate outputsignals conveying information related to a first vehicle operator. Theprocessor(s) may be configured to make a first type of determination ofat least one of an object on which attention of the first vehicleoperator is focused during operation of the first vehicle and/or adirection in which attention of the first vehicle operator is focusedduring operation of the first vehicle. The first type of determinationmay be made multiple times in an ongoing manner and/or at regularintervals spanning at least 50 percent of a period of operation of thefirst vehicle, or another percentage. The first type of determinationmay be based on the generated output signals. The period may span atleast a minute. The processor(s) may be configured to make a second typeof determination regarding fatigue of the first vehicle operator duringoperation of the first vehicle. The second type of determination may bemade multiple times in a second ongoing manner and/or at particularintervals spanning at least 50 percent of the period of operation of thefirst vehicle, or another percentage. The second type of determinationmay be based on the generated output signals. The processor(s) may beconfigured to make a third type of determination of at least one of adistraction level of the first vehicle operator and/or a fatigue levelof the first vehicle operator. In some implementations, the distractionlevel may be based on the first type of determination. In someimplementations, the fatigue level may be based on the second type ofdetermination. The processor(s) may be configured to effectuate anotification regarding the third type of determination, responsive to atleast one of the distraction level breaching a distraction thresholdand/or the fatigue level breaching a fatigue threshold, to at least oneof the first vehicle operator, a remote computing server, and/or anotherdestination.

Another aspect of the present disclosure relates to a method forverifying whether vehicle operators are paying attention duringoperation of a first vehicle. The method may include generating outputsignals conveying information related to a first vehicle operator. Themethod may include making a first type of determination of at least oneof an object on which attention of the first vehicle operator is focusedduring operation of the first vehicle and/or a direction in whichattention of the first vehicle operator is focused during operation ofthe first vehicle. The first type of determination may be made multipletimes in an ongoing manner and/or at regular intervals spanning at least50 percent of a period of operation of the first vehicle, or anotherpercentage. The first type of determination may be based on thegenerated output signals. The period may span at least a minute. Themethod may include making a second type of determination regardingfatigue of the first vehicle operator during operation of the firstvehicle. The second type of determination may be made multiple times ina second ongoing manner and/or at particular intervals spanning at least50 percent of the period of operation of the first vehicle, or anotherpercentage. The second type of determination may be based on thegenerated output signals. The method may include making a third type ofdetermination of at least one of a distraction level of the firstvehicle operator and/or a fatigue level of the first vehicle operator.In some implementations, the distraction level may be based on the firsttype of determination. In some implementations, the fatigue level may bebased on the second type of determination. The method may includeeffectuating a notification regarding the third type of determination,responsive to at least one of the distraction level breaching adistraction threshold and/or the fatigue level breaching a fatiguethreshold, to at least one of the first vehicle operator, a remotecomputing server, and/or another destination.

As used herein, any association (or relation, or reflection, orindication, or correspondency) involving servers, processors, clientcomputing platforms, output signals, sensors, determinations,detections, vehicles, vehicle operators, directions, distraction levels,fatigue levels, alertness levels, readiness levels, periods ofoperation, notifications, challenges, responses, modifications, and/oranother entity or object that interacts with any part of the systemand/or plays a part in the operation of the system, may be a one-to-oneassociation, a one-to-many association, a many-to-one association,and/or a many-to-many association or N-to-M association (note that N andM may be different numbers greater than 1).

As used herein, the term “obtain” (and derivatives thereof) may includeactive and/or passive retrieval, determination, derivation, transfer,upload, download, submission, and/or exchange of information, and/or anycombination thereof. As used herein, the term “effectuate” (andderivatives thereof) may include active and/or passive causation of anyeffect. As used herein, the term “determine” (and derivatives thereof)may include measure, calculate, compute, estimate, approximate,generate, and/or otherwise derive, and/or any combination thereof.

These and other features, and characteristics of the present technology,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, will become more apparent upon consideration of thefollowing description and the appended claims with reference to theaccompanying drawings, all of which form a part of this specification,wherein like reference numerals designate corresponding parts in thevarious figures. It is to be expressly understood, however, that thedrawings are for the purpose of illustration and description only andare not intended as a definition of the limits of the invention. As usedin the specification and in the claims, the singular form of “a”, “an”,and “the” include plural referents unless the context clearly dictatesotherwise.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system configured for verifying whether vehicleoperators are paying attention during operation of a vehicle, inaccordance with one or more implementations.

FIG. 2 illustrates a method for verifying whether vehicle operators arepaying attention during operation of a vehicle, in accordance with oneor more implementations.

FIG. 3A-3B illustrate exemplary graphs of different types ofdeterminations over time, pertaining to a vehicle operator, as may beused by a system configured for verifying whether vehicle operators arepaying attention during operation of a vehicle, in accordance with oneor more implementations.

DETAILED DESCRIPTION

FIG. 1 illustrates a system 100 configured for verifying whether vehicleoperators are paying attention, in accordance with one or moreimplementations. As described herein, verifying whether an individualvehicle operator is paying attention may be done in an ongoing manner,e.g., continuously during operation of the vehicle, and/orintermittently during operation of the vehicle. By virtue of thisverification, an individual vehicle operator may pay more attention. Inother words, testing for alertness and/or readiness may improve thealertness and/or readiness.

In some implementations, system 100 may include one or more of server(s)102, electronic storage 126, a set of sensors 108, network(s) 13, clientcomputing platform(s) 104, external resources 124, a remote computingserver 125, and/or other components. System 100 and/or componentsthereof may be carried and/or otherwise supported by one or morevehicles (e.g., a first vehicle, a second vehicle, a third vehicle, andso forth), including but not limited to a vehicle 12. In someimplementations, individual vehicles (e.g., vehicle 12) may carry and/orotherwise support system 100 and/or components thereof. Server(s) 102may be configured to communicate with one or more client computingplatforms 104 according to a client/server architecture and/or otherarchitectures. Client computing platform(s) 104 may be configured tocommunicate with other client computing platforms via server(s) 102and/or according to a peer-to-peer architecture and/or otherarchitectures. In some implementations, users may access system 100 viaclient computing platform(s) 104.

Individual vehicles may include a set of resources for data processingand/or electronic storage, including but not limited to persistentstorage. Individual vehicles may include a set of sensors (e.g., set ofsensors 108). In some implementations, individual vehicles may beconfigured to detect vehicle events, e.g., based on output signalsgenerated by one or more sensors.

Set of sensors 108 may be configured to generate output signalsconveying information related to (operation of) vehicle 12, a vehicleoperator of vehicle 12, and/or a context of vehicle 12 (e.g., related tothe surroundings of vehicle 12). In some implementations, operation ofvehicle 12 may be actively and primarily controlled by a vehicleoperator (i.e., a human operator). In some implementations, operation ofvehicle 12 may be actively and primarily controlled by an autonomousdriving algorithm. In such a case, a human vehicle operator may takeover (or be requested to take over) control of the autonomous drivingalgorithm, e.g., responsive to extreme and/or unconventional drivingscenarios, or responsive to a failure or error-condition of theautonomous driving algorithm. In some implementations, a human vehicleoperator and an autonomous driving algorithm may form a team thatcontrols operations of vehicle 12 together.

Information related to the operation of vehicle 12 may include feedbackinformation from one or more of the mechanical systems of vehicle 12,and/or other information. The mechanical systems of vehicle 12 mayinclude, for example, the engine, the drive train, the lighting systems(e.g., headlights, brake lights), the braking system, the transmission,fuel delivery systems, and/or other mechanical systems. The mechanicalsystems of vehicle 12 may include one or more mechanical sensors,electronic sensors, and/or other sensors that generate the outputsignals (e.g., seat belt sensors, tire pressure sensors, etc.). In someimplementations, at least one of sensors 14 may be a vehicle systemsensor included in an Engine Control Module (ECM) system of vehicle 12.

In some implementations, set of sensors 108 may generate output signalsconveying information related to a vehicle operator of vehicle 12, suchas visual information, motion-related information, position-relatedinformation, biometric information, medical information, and/or otherinformation. In some implementations, set of sensors 108 may include oneor more sensors configured to generate output signals that conveyinformation related to biological activity of the vehicle operator. Insome implementations, such sensors may be wearable by the vehicleoperator. In some implementations, such sensors may be placed inphysical proximity to the vehicle operator to facilitate monitoring thebiological activity of the vehicle operator. The information related tothe biological activity of the vehicle operator may include heart rate,respiration rate, verbal expressions, responses to conditions in thephysical environment in and/or around vehicle 12, and/or othercharacteristics of the vehicle operator. For example, one or moresensors in set of sensors 108 may generate an output based on a heartrate of the vehicle operator (e.g., a particular sensor may be a heartrate sensor located on the chest of the vehicle operator, and/or beconfigured as an optical sensor included in a bracelet on a wrist of thevehicle operator, and/or be located on another limb of the vehicleoperator), movement of the vehicle operator (e.g., a particular sensormay include a bracelet around the wrist and/or ankle of the vehicleoperator with an accelerometer such that physical reactions may beanalyzed using actigraphy signals), changes in skin color of the vehicleoperator (e.g., set of sensors 108 may include a camera that can detectchanges in skin color of the vehicle operator and infer vital signs suchas heart rate, breathing rate, and/or other vital signs from the changesin color), respiration of the vehicle operator, brain waves of thevehicle operator (e.g., a particular sensor may generate output signalsrelated to an electroencephalogram (EEG) of the vehicle operator),and/or other characteristics of the vehicle operator.

In some implementations, set of sensors 108 may generate output signalsconveying information related to the context of vehicle 12, such asinformation related to the environment in and/or around vehicle 12. Thevehicle environment may include spaces in and around an interior and anexterior of vehicle 12. The information related to the context ofvehicle 12 may include information related to movement of vehicle 12, anorientation of vehicle 12, a geographic position of vehicle 12, aspatial position of vehicle 12 relative to other objects, a tilt angleof vehicle 12, an inclination/declination angle of vehicle 12, and/orother information. In some implementations, the output signals conveyingthe information related to the context of vehicle 12 may be generatedvia non-standard aftermarket sensors installed in vehicle 12. Set ofsensors 108 may include, for example, one or more video cameras, one ormore microphones, an accelerometer, a gyroscope, a geolocation sensor(e.g., a Global Positioning System or GPS device), a radar detector, amagnetometer, radar (e.g., for measuring distance of a leading vehicle),and/or other sensors.

Individual sensors in set of sensors 108 may include, by way ofnon-limiting example, one or more of an altimeter (e.g. a sonicaltimeter, a radar altimeter, and/or other types of altimeters), abarometer, a magnetometer, a pressure sensor (e.g. a static pressuresensor, a dynamic pressure sensor, a pitot sensor, etc.), a thermometer,an accelerometer, a gyroscope, an inertial measurement sensor, globalpositioning system sensors, a tilt sensor, a motion sensor, a vibrationsensor, an image sensor, a camera, an ultrasonic sensor, an infraredsensor, a light sensor, a microphone, an air speed sensor, a groundspeed sensor, an altitude sensor, medical sensors (including but notlimited to blood pressure sensor, pulse oximeter, heart rate sensor,etc.), degree-of-freedom sensors (e.g. 6-DOF and/or 9-DOF sensors), acompass, and/or other sensors. As used herein, the term “motion sensor”may include one or more sensors configured to generate output conveyinginformation related to position, location, distance, motion, movement,acceleration, and/or other motion-based parameters. Output signalsgenerated by individual sensors (and/or information based thereon) maybe stored and/or transferred in electronic files.

In some implementations, individual sensors in set of sensors 108 mayinclude image sensors, cameras, depth sensors, remote sensors, and/orother sensors. As used herein, the terms “camera” and/or “image sensor”may include any device that captures image information, including butnot limited to a single lens-based camera, a camera array, a solid-statecamera, a mechanical camera, a digital camera, an image sensor, a depthsensor, a remote sensor, a lidar, an infrared sensor, a (monochrome)complementary metal-oxide-semiconductor (CMOS) sensor, an active pixelsensor, and/or other sensors. Individual sensors may be configured tocapture information, including but not limited to visual information,video information, audio information, geolocation information,orientation and/or motion information, depth information, distanceinformation, and/or other information. Information captured by one ormore sensors may be marked, timestamped, annotated, and/or otherwiseprocessed such that information captured by other sensors can besynchronized, aligned, annotated, and/or otherwise associated therewith.For example, video information captured by an image sensor may besynchronized with information captured by an accelerometer or othersensor. In some implementations, set of sensors 108 may include multiplecameras positioned around the vehicle and synchronized together toprovide a 360-degree view of the inside of a vehicle and/or a 360-degreeview of the outside of a vehicle. In some implementations, an imagesensor may be integrated with electronic storage such that capturedinformation may be (processed and) stored in the integrated embeddedstorage. In some implementations, an image sensor may be configured totransfer captured information to remote electronic storage media, e.g.through “the cloud.”

Although set of sensors 108 is depicted in FIG. 1 as a single element,this is not intended to be limiting. In some implementations, set ofsensors 108 may be configured to generate output signals continuously,in an on-going manner, and/or at regular or irregular intervals duringoperation of vehicle 12. In some implementations, set of sensors 108 maybe configured to generate output signals multiple times in an ongoingmanner and/or at regular intervals spanning at least 50 percent of aperiod of operation of vehicle 12. In some implementations, thatpercentage may be 80 percent, 90 percent, 95 percent, and/or anotherpercentage. In some implementations, the period of operation of vehicle12 may coincide with movement of vehicle 12. In some implementations,the period of operation of vehicle 12 may coincide with the enginestatus of vehicle 12 (e.g., engine turned on). In some implementations,the period of operation of vehicle 12 may coincide with the ignitionstatus of vehicle 12 (e.g., ignition turned on). In someimplementations, the period of operation of vehicle 12 may coincide withthe electronic status of vehicle 12 (e.g., electronics turned on). Insome implementations, the period of operation of vehicle 12 may coincidewith a trip of vehicle 12. In some implementations, the period ofoperation of vehicle 12 may coincide with vehicle 12 being actively andprimarily controlled by a human vehicle operator.

Server(s) 102 may be configured by machine-readable instructions 106.Machine-readable instructions 106 may include one or more instructioncomponents. The instruction components may include computer programcomponents. The instruction components may include one or more ofattention component 110, notification component 112, alertness-readinesscomponent 114, challenge component 116, response detection component118, threshold modification component 120, alertness detectionmodification component 122, and/or other instruction components.

Attention component 110 may be configured to make different types ofdeterminations during operation of vehicle 12. The different types ofdeterminations may include a first type of determinations, a second typeof determinations, a third type of determinations, and/or other types ofdeterminations. Determinations by attention component 110 may bedetermined based on output signals generated by set of sensors 108. Insome implementations, different types of determinations by attentioncomponent 110 may be made multiple times in an ongoing manner and/or atregular intervals spanning at least a particular percentage of a periodof operation of vehicle 12. In some implementations, the particularpercentage may be 50 percent, 60 percent, 70 percent, 80 percent, 90percent, 95 percent, and/or another percentage. In some implementations,the period of operation of vehicle 12 may span at least a minute, atleast 30 minutes, at least an hour, and/or another period of time. Insome implementations, the period of operation of vehicle 12 may span anindividual trip of vehicle 12. For example, in some implementations, atrip may be defined by continuous engine operation. For example, a tripmay start at the moment of ignition, and end when the engine is turnedoff. For example, in some implementations, a trip may be defined bycontinuous movement. For example, a trip may start at the moment avehicle starts moving, and end when the vehicle stops moving for morethan a particular period (such as, e.g., 1 minute, 5 minutes, 10minutes, and/or another period of time). Other definitions of the startand end of an individual trip are envisioned within the scope of thisdisclosure.

In some implementations, the first type of determinations by attentioncomponent 110 may include determinations of an object on which attentionof the vehicle operator is focused during operation of vehicle 12.Alternatively, and/or simultaneously, the first type of determinationsmay include determinations of a direction in which attention of thevehicle operator is focused during operation of the vehicle 12. Objectson which attention of the vehicle operator is focused may includeobjects within vehicle 12 and objects outside of vehicle 12. By way ofnon-limiting example, objects within vehicle 12 may include the steeringwheel, the dashboard, the speedometer, the navigation system, themirrors, the radio, a user interface that is part of vehicle 12, thecenter console, the gearshift, other passengers, the phone of thevehicle operator, and/or other objects. By way of non-limiting example,objects outside of vehicle 12 may include the road ahead of vehicle 12,signage on or near the road, other vehicles on or near the road,advertisements along the road, clouds, birds, buildings, and/or otherobjects. For longer periods of time, determinations of this first typemay form a sequence of objects on which attention of the vehicleoperator is focused in succession. For example, a particular sequencemay include the road, the speedometer, the road, the rear-view mirror,the road, the vehicle operator's phone, signs along the side of theroad, an overhead traffic light, nearby traffic, the road, a sidemirror, the radio, the road, and so forth.

In some implementations, determinations by attention component 110 of adirection in which attention of the vehicle operator is focused duringoperation of the vehicle 12 may be based on analyzing the positionand/or orientation of one or more of the individual vehicle operator,the head of the individual vehicle operator, the eyes of the individualvehicle operator, and/or other parts of the individual vehicle operator.For example, an image sensor may capture visual information thatincludes at least part of the individual vehicle operator, and suchvisual information may be analyzed, e.g. using computer visiontechniques, three-dimensional modeling techniques, signal-processingtechniques, pattern recognition, and/or other mechanisms of analyzingvisual information (including but not limited to automated systems thatapproximate functionality that the human visual system can perform). Insome implementations, such a direction may be a vector, e.g., atwo-dimensional or three-dimensional vector originating from theindividual vehicle operator. In some implementations, an individualdetermination of such a direction may be made once per second, once per3 seconds, once per 5 seconds, once per 10 seconds, and/or using otherintervals. For longer periods of time, determinations of thesedirections may form a sequence of directions in which attention of thevehicle operator is focused in succession. For example, a particularsequence may include the general direction of the road ahead, thegeneral direction of the dashboard, the general direction of the roadahead, the general direction of a mirror, the general direction of thevehicle operator's lap or phone, the general direction of the passengersin the back of the vehicle, and so forth.

In some implementations, determinations by attention component 110 ofdirections in which attention of the vehicle operator is focused may beused to determine the object on which attention of the vehicle operatoris focused.

In some implementations, the second type of determinations by attentioncomponent 110 may include determinations regarding fatigue of thevehicle operator of vehicle 12. In some implementations, determinationsregarding fatigue may be based on detections of fatigue indicators. Insome implementations, individual fatigue indicators may be momentary orfleeting. By way of non-limiting example, fatigue indicators may includeone or more of the eyes of the individual vehicle operator being closed,the rate of blinking, the eyes of the individual operator momentarilyopening extra wide, a particular heart rate of the individual vehicleoperator (e.g., the heart rate slowing down during driving), aparticular breathing rate of the individual vehicle operator, (e.g., thebreathing rate slowing down during driving), head bobbing by theindividual vehicle operator, vigorous head shaking by the individualvehicle operator, a slumped posture of the individual vehicle operator,certain body movements by the individual vehicle operator, and/or otherindicators. In some implementations, such detections may be based onanalyzing one or more of captured visual information, generated outputsignals from biometric sensors, and/or other information.

In some implementations, the third type of determinations by attentioncomponent 110 may include determinations of a distraction level of thevehicle operator of vehicle 12. Alternatively, and/or simultaneously,the third type of determinations by attention component 110 may includedeterminations of a fatigue level of the vehicle operator of vehicle 12.

For example, the distraction level may be determined based on ananalysis of the first type of determinations by attention component 110,and/or on other information. In some implementations, individualdeterminations of the first type may correspond to numerical scores,which may be aggregated to determine the distraction level. By way ofnon-limiting example, objects on which attention of an individualvehicle operator arguably should be focused such as the steering wheelor the road ahead may correspond to a low numerical score, e.g., from0-10. By way of non-limiting example, directions in which attention ofthe individual vehicle operator arguably should be focused such as thegeneral direction of the road ahead may correspond to a low numericalscore, e.g., from 0-10. By way of non-limiting example, objects on whichattention of the individual vehicle operator arguably should not befocused (or not for long) such as the radio or a phone may correspond toa high numerical score, e.g., from80-90. By way of non-limiting example,directions in which attention of the individual vehicle operatorarguably should not be focused (or not for long) such as the passengersin the back of vehicle 12 may correspond to a high numerical score,e.g., from90-100. By aggregating scores over time, the distraction levelmay be determined as a numerical score over time, say, between 0 and100. By way of non-limiting example, FIG. 3A illustrates a graph 35 thatcould be of a distraction level determined over time, pertaining to aparticular vehicle operator.

For example, the fatigue level may be determined based on an analysis ofthe second type of determinations by attention component 110, and/or onother information. In some implementations, individual determinations ofthe second type may correspond to numerical scores, which may beaggregated to determine the fatigue level. By way of non-limitingexample, fatigue indicators indicating a low level of fatigue such asactive head moment between appropriate objects and/or directions ofattention may correspond to a low numerical score, e.g., from 0-10. Byway of non-limiting example, fatigue indicators indicating a high levelof fatigue such as eyes being closed for more than two seconds maycorrespond to a high numerical score, e.g., from from80-90. By way ofnon-limiting example, certain fatigue indicators such as the vehicleoperator slumping in his or her seat may correspond to a high numericalscore, e.g., from90-100. By aggregating scores over time, the fatiguelevel may be determined as a numerical score, say, between 0 and 100. Byway of non-limiting example, FIG. 3A illustrates a graph 35 that couldbe of a fatigue level determined over time, pertaining to a particularvehicle operator.

Notification component 112 may be configured to effectuatenotifications. The notifications may include notifications to individualvehicle operators, one or more fleet managers, remote computing servers,and/or other destinations. In some implementations, notifications bynotification component 112 may occur responsive to one or moreconditions. For example, a particular notification may be transmittedresponsive to the distraction level breaching a distraction threshold.For example, a particular notification may be made responsive to thefatigue level breaching a fatigue threshold. In some implementations,notification component 112 may be configured to compare the distractionlevel with a distraction threshold, the fatigue level with a fatiguethreshold, and/or other levels with other thresholds. By way ofnon-limiting example, FIG. 3B illustrates a distraction level 31 and afatigue level 32 determined over time (so that these levels overlap),pertaining to a particular vehicle operator. Distraction level 31 may becompared with a distraction threshold 31 a, which is breached at amoment 31 b. Fatigue level 32 may be compared with a fatigue threshold32 a, which is breached at a moment 32 b and at a moment 32 c. At moment32 b distraction level 31 is below distraction threshold 31 a, but atmoment 32 c both distraction level 31 and fatigue level 32 are abovetheir respective threshold levels. In some implementations, notificationcomponent 112 may be configured to effectuate notifications at allbreaches, which occurs in FIG. 3B at moment 31 b, moment 32 b, andmoment 32 c. In some implementations, notification component 112 may beconfigured to effectuate notifications at all simultaneous breaches ofmultiple thresholds, which occurs in FIG. 3B at moment 32 c. Otherlogical combinations and/or sequences of different breaches ofthresholds are envisioned within the scope of this disclosure.

Referring to FIG. 1, alertness-readiness component 114 may be configuredto determine alertness levels of individual vehicle operators. Thealertness level may represent whether a particular vehicle operator ispaying attention in a manner that is appropriate for the particularvehicle operator being in active and primary control of the operation ofvehicle 12. By way of non-limiting example, the alertness level may bebased on one or more of the first type of determination, the second typeof determination, and the third type of determination. For example, insome implementations, a particular alertness level may be an aggregationof the distraction level and the fatigue level of a particular vehicleoperator. In some implementations, notification component 112 may beconfigured to compare the alertness level with an alertness threshold,and/or effectuate a notification responsive to a breach of the alertnessthreshold. For example, a particular alertness threshold may be ifeither the distraction level or the fatigue level is more than 35, or iftheir combined total is more than 50.

In some implementations, alertness-readiness component 114 may beconfigured to determine readiness levels of individual vehicleoperators. The readiness level may represent whether a particularvehicle operator is paying attention in a particular manner that isappropriate for vehicle 12 being actively and primarily controlled by anautonomous driving algorithm. By way of non-limiting example, thereadiness level may be based on one or more of the first type ofdetermination, the second type of determination, and the third type ofdetermination. For example, in some implementations, a particularreadiness level may be an aggregation of the distraction level and thefatigue level of a particular vehicle operator. In some implementations,notification component 112 may be configured to compare the readinesslevel with a readiness threshold, and/or effectuate a notificationresponsive to a breach of the readiness threshold. For example, aparticular readiness threshold may be if either the distraction level orthe fatigue level is more than 50, or if their combined total is morethan 75. In some implementations, the readiness threshold may be higherthan the alertness threshold.

Challenge component 116 may be configured to present challenges to thevehicle operators. In some implementations, challenges may includeauditory and/or visual notifications for vehicle operators. In someimplementations, challenges may be issued through user interfaces invehicles, such as a particular user interface (not shown in FIG. 1) invehicle 12. For example, a particular challenge may be a question posedto the vehicle operator, or a pattern presented to the particularvehicle operator. Individual challenges may correspond to expectedresponses by the vehicle operators, including but not limited toparticular interactions by the particular vehicle operator with a userinterface. For example, a challenge may be a simple arithmetic question,such as “what is 2 times 3?”. In some implementations, a notification bynotification component 112 may act as and/or include a challenge.

Response detection component 118 may be configured to detectinteractions and/or other responses by vehicle operators. In someimplementations, response detection component 118 may be configured todetect whether a particular vehicle operator provides a response to aparticular challenge (presented by challenge component 116), and/orwhether that particular response matches the corresponding expectedresponse. In some implementations, responses by vehicle operators may beprovided through a user interface. In some implementations, one or moreof the second type of determination and the third type of determination(by attention component 110) may be further based on a detected responseto a challenge. For example, the detected response to the arithmeticquestion above may be analyzed both for correctness and forreaction-speed (e.g., the time elapsed between presentation of thechallenge and detection of the response). Incorrect responses may reducethe distraction level, the fatigue level, and/or other levels. Slowresponses may reduce the distraction level, the fatigue level, and/orother levels.

Threshold modification component 120 may be configured to modifythresholds, including but not limited to the distraction threshold, thefatigue threshold, the alertness threshold, the readiness threshold,and/or other thresholds. In some implementations, modifications bythreshold modification component 120 may be based on at least one of aspeed of vehicle 12, traffic conditions around vehicle 12, local weatherconditions, and/or local road surface conditions. For example, thefatigue threshold may be lowered in response to vehicle 12 going over 50MPH. Alternatively, and/or simultaneously, in some implementations,modifications by threshold modification component 120 may be based onresponses detected by response detection component 118. For example, thedistraction threshold may be lowered in response to incorrect and/orslow responses. Alternatively, and/or simultaneously, in someimplementations, modifications by threshold modification component 120may be based on one or more thresholds being breached. For example, thefatigue threshold may be lowered responsive to the distraction thresholdbeing breached.

In some implementations, alertness detection modification component 122may be configured to modify determination of the alertness level basedon at least one of a speed of vehicle 12, traffic conditions around thevehicle 12, local weather conditions, local road surface conditions,and/or other conditions or detections. For example, alertness detectionmodification component 112 may modify the operation ofalertness-readiness component 114, e.g., by modifying one or morethresholds. Alternatively, and/or simultaneously, in someimplementations, alertness detection modification component 122 may beconfigured to modify determination of the readiness level based on atleast one of a speed of vehicle 12, traffic conditions around thevehicle 12, local weather conditions, local road surface conditions,and/or other conditions or detections.

In some implementations, server(s) 102, client computing platform(s)104, and/or external resources 124 may be operatively linked via one ormore electronic communication links. For example, such electroniccommunication links may be established, at least in part, via a networksuch as the Internet and/or other networks. It will be appreciated thatthis is not intended to be limiting, and that the scope of thisdisclosure includes implementations in which server(s) 102, clientcomputing platform(s) 104, and/or external resources 124 may beoperatively linked via some other communication media.

A given client computing platform 104 may include one or more processorsconfigured to execute computer program components. The computer programcomponents may be configured to enable an expert or user associated withthe given client computing platform 104 to interface with system 100and/or external resources 124, and/or provide other functionalityattributed herein to client computing platform(s) 104. By way ofnon-limiting example, the given client computing platform 104 mayinclude one or more of a desktop computer, a laptop computer, a handheldcomputer, a tablet computing platform, a NetBook, a Smartphone, a smartwatch, a gaming console, and/or other computing platforms.

External resources 124 may include sources of information outside ofsystem 100, external entities participating with system 100, and/orother resources. In some implementations, some or all of thefunctionality attributed herein to external resources 124 may beprovided by resources included in system 100.

Remote computing server 125 may be separate, discrete, and/or distinctfrom individual vehicles (such as vehicle 12), and/or system 100. Insome implementations, remote computing server 125 may be configured toreceive, analyze, and/or otherwise process information from one of morevehicles, including but not limited to vehicle 12. In someimplementations, remote computing server 125 may be configured toreceive notifications from vehicle 12, e.g., regarding determinationspertaining to whether the vehicle operator of vehicle 12 is and/or hasbeen paying attention.

Server(s) 102 may include electronic storage 126, one or more processors128, and/or other components. Server(s) 102 may include communicationlines, or ports to enable the exchange of information with a networkand/or other computing platforms. Illustration of server(s) 102 in FIG.1 is not intended to be limiting. Server(s) 102 may include a pluralityof hardware, software, and/or firmware components operating together toprovide the functionality attributed herein to server(s) 102. Forexample, server(s) 102 may be implemented by a cloud of computingplatforms operating together as server(s) 102.

Electronic storage 126 may comprise non-transitory storage media thatelectronically stores information. The electronic storage media ofelectronic storage 126 may include one or both of system storage that isprovided integrally (i.e., substantially non-removable) with server(s)102 and/or removable storage that is removably connectable to server(s)102 via, for example, a port (e.g., a USB port, a firewire port, etc.)or a drive (e.g., a disk drive, etc.). Electronic storage 126 mayinclude one or more of optically readable storage media (e.g., opticaldisks, etc.), magnetically readable storage media (e.g., magnetic tape,magnetic hard drive, floppy drive, etc.), electrical charge-basedstorage media (e.g., EEPROM, RAM, etc.), solid-state storage media(e.g., flash drive, etc.), and/or other electronically readable storagemedia. Electronic storage 126 may include one or more virtual storageresources (e.g., cloud storage, a virtual private network, and/or othervirtual storage resources). Electronic storage 126 may store softwarealgorithms, information determined by processor(s) 128, informationreceived from server(s) 102, information received from client computingplatform(s) 104, and/or other information that enables server(s) 102 tofunction as described herein.

Processor(s) 128 may be configured to provide information processingcapabilities in server(s) 102. As such, processor(s) 128 may include oneor more of a digital processor, an analog processor, a digital circuitdesigned to process information, an analog circuit designed to processinformation, a state machine, and/or other mechanisms for electronicallyprocessing information. Although processor(s) 128 is shown in FIG. 1 asa single entity, this is for illustrative purposes only. In someimplementations, processor(s) 128 may include a plurality of processingunits. These processing units may be physically located within the samedevice, or processor(s) 128 may represent processing functionality of aplurality of devices operating in coordination. Processor(s) 128 may beconfigured to execute components 110, 112, 114, 116, 118, 120, and/or122, and/or other components. Processor(s) 128 may be configured toexecute components 110, 112, 114, 116, 118, 120, and/or 122, and/orother components by software; hardware; firmware; some combination ofsoftware, hardware, and/or firmware; and/or other mechanisms forconfiguring processing capabilities on processor(s) 128. As used herein,the term “component” may refer to any component or set of componentsthat perform the functionality attributed to the component. This mayinclude one or more physical processors during execution of processorreadable instructions, the processor readable instructions, circuitry,hardware, storage media, or any other components.

It should be appreciated that although components 110, 112, 114, 116,118, 120, and/or 122 are illustrated in FIG. 1 as being implementedwithin a single processing unit, in implementations in whichprocessor(s) 128 includes multiple processing units, one or more ofcomponents 110, 112, 114, 116, 118, 120, and/or 122 may be implementedremotely from the other components. The description of the functionalityprovided by the different components 110, 112, 114, 116, 118, 120,and/or 122 described below is for illustrative purposes, and is notintended to be limiting, as any of components 110, 112, 114, 116, 118,120, and/or 122 may provide more or less functionality than isdescribed. For example, one or more of components 110, 112, 114, 116,118, 120, and/or 122 may be eliminated, and some or all of itsfunctionality may be provided by other ones of components 110, 112, 114,116, 118, 120, and/or 122. As another example, processor(s) 128 may beconfigured to execute one or more additional components that may performsome or all of the functionality attributed below to one of components110, 112, 114, 116, 118, 120, and/or 122.

FIG. 2 illustrates a method 200 for verifying whether vehicle operatorsare paying attention, in accordance with one or more implementations.The operations of method 200 presented below are intended to beillustrative. In some implementations, method 200 may be accomplishedwith one or more additional operations not described, and/or without oneor more of the operations discussed. Additionally, the order in whichthe operations of method 200 are illustrated in FIG. 2 and describedbelow is not intended to be limiting.

In some implementations, method 200 may be implemented in one or moreprocessing devices (e.g., a digital processor, an analog processor, adigital circuit designed to process information, an analog circuitdesigned to process information, a state machine, and/or othermechanisms for electronically processing information). The one or moreprocessing devices may include one or more devices executing some or allof the operations of method 200 in response to instructions storedelectronically on an electronic storage medium. The one or moreprocessing devices may include one or more devices configured throughhardware, firmware, and/or software to be specifically designed forexecution of one or more of the operations of method 200.

An operation 202 may include generating output signals conveyinginformation related to a first vehicle operator. Operation 202 may beperformed by a set of sensors that is the same as or similar to set ofsensors 108, in accordance with one or more implementations.

An operation 204 may include making a first type of determination of atleast one of an object on which attention of the first vehicle operatoris focused during operation of a first vehicle and/or a direction inwhich attention of the first vehicle operator is focused duringoperation of the first vehicle. The first type of determination may bemade multiple times in an ongoing manner and/or at regular intervalsspanning at least 50 percent of a period of operation of the firstvehicle. The first type of determination may be based on the generatedoutput signals. The period may span at least a minute. Operation 204 maybe performed by one or more hardware processors configured bymachine-readable instructions including a component that is the same asor similar to attention component 110, in accordance with one or moreimplementations.

An operation 206 may include making a second type of determinationregarding fatigue of the first vehicle operator during operation of thefirst vehicle. The second type of determination may be made multipletimes in a second ongoing manner and/or at particular intervals spanningat least 50 percent of the period of operation of the first vehicle. Thesecond type of determination may be based on the generated outputsignals. Operation 206 may be performed by one or more hardwareprocessors configured by machine-readable instructions including acomponent that is the same as or similar to attention component 110, inaccordance with one or more implementations.

An operation 208 may include making a third type of determination of atleast one of a distraction level of the first vehicle operator and/or afatigue level of the first vehicle operator. The distraction level maybe based on the first type of determination. The fatigue level may bebased on the second type of determination. Operation 208 may beperformed by one or more hardware processors configured bymachine-readable instructions including a component that is the same asor similar to attention component 110, in accordance with one or moreimplementations.

An operation 210 may include effectuating a notification regarding thethird type of determination, responsive to at least one of thedistraction level breaching a distraction threshold and/or the fatiguelevel breaching a fatigue threshold, to at least one of the firstvehicle operator and/or a remote computing server. Operation 210 may beperformed by one or more hardware processors configured bymachine-readable instructions including a component that is the same asor similar to notification component 112, in accordance with one or moreimplementations.

Although the present technology has been described in detail for thepurpose of illustration based on what is currently considered to be themost practical and preferred implementations, it is to be understoodthat such detail is solely for that purpose and that the technology isnot limited to the disclosed implementations, but, on the contrary, isintended to cover modifications and equivalent arrangements that arewithin the spirit and scope of the appended claims. For example, it isto be understood that the present technology contemplates that, to theextent possible, one or more features of any implementation can becombined with one or more features of any other implementation.

What is claimed is:
 1. A system configured for verifying whether vehicleoperators are paying attention, the system comprising: a set of sensorsconfigured to generate output signals conveying information related to afirst vehicle operator; and one or more hardware processors configuredby machine-readable instructions to: make, by an attention component, afirst type of determination of at least one of: (i) an object on whichattention of the first vehicle operator is focused during operation of afirst vehicle, and (ii) a direction in which attention of the firstvehicle operator is focused during operation of the first vehicle,wherein the first type of determination is made multiple times intervalsspanning at least 50 percent of a period of operation of the firstvehicle, wherein the first type of determination is based on thegenerated output signals, and wherein the period spans at least aminute; determine a distraction level of the first vehicle operatorbased on the first type of determination, wherein the distraction levelis represented by a first numerical score; make, by the attentioncomponent, a second type of determination regarding fatigue of the firstvehicle operator during operation of the first vehicle, wherein thesecond type of determination is made multiple times at particularintervals spanning at least 50 percent of the period of operation of thefirst vehicle, wherein the second type of determination is based on thegenerated output signals; determine a fatigue level of the first vehicleoperator based on the second type of determination, wherein the fatiguelevel is represented by a second numerical score; combine the firstnumerical score with the second numerical score to determine anaggregated numerical score; make, by the attention component, a thirdtype of determination whether the aggregated numerical score hasbreached a threshold level; and effectuate a notification, by anotification component, regarding the third type of determination,responsive to determining that the aggregated numerical score hasbreached the threshold level, to at least one of: (i) the first vehicleoperator, and (ii) a remote computing server.
 2. The system of claim 1,wherein the first vehicle operator is actively participating in theoperation of the first vehicle during the period of operation.
 3. Thesystem of claim 1, wherein operation of the first vehicle is activelyand primarily controlled by an autonomous driving algorithm.
 4. Thesystem of claim 1, wherein the period of operation of the first vehiclespans a trip of the first vehicle.
 5. The system of claim 1, wherein theone or more hardware processors are further configured bymachine-readable instructions to: determine, by an alertness-readinesscomponent, an alertness level of the first vehicle operator, wherein thealertness level represents whether the first vehicle operator is payingattention in a manner that is appropriate for the first vehicle operatorbeing in active and primary control of the operation of the firstvehicle, wherein the alertness level is based on one or more of thefirst type of determination, the second type of determination, and thethird type of determination.
 6. The system of claim 1, wherein the oneor more hardware processors are further configured by machine-readableinstructions to: determine, by an alertness-readiness component, areadiness level of the first vehicle operator, wherein the readinesslevel represents whether the first vehicle operator is paying attentionin a particular manner that is appropriate for the first vehicle beingactively and primarily controlled by an autonomous driving algorithm,wherein the readiness level is based on one or more of the first type ofdetermination, the second type of determination, and the third type ofdetermination.
 7. The system of claim 1, wherein the one or morehardware processors are further configured by machine-readableinstructions to: present, by a challenge component, a challenge to thefirst vehicle operator, wherein the challenge corresponds to an expectedresponse by the first vehicle operator, wherein the expected responseincludes a particular interaction by the first vehicle operator with auser interface; detect, by a response detection component, whether thefirst vehicle operator provides a response to the challenge that matchesthe expected response, wherein the response is provided through the userinterface; wherein one or more of the second type of determination andthe third type of determination is further based on the detectedresponse to the challenge.
 8. The system of claim 7, wherein thenotification includes the challenge.
 9. The system of claim 1, whereinthe one or more hardware processors are further configured bymachine-readable instructions to: modify, by a threshold modificationcomponent, the distraction threshold or the fatigue threshold based onat least one of: (i) a speed of the first vehicle, (ii) trafficconditions around the first vehicle, (iii) local weather conditions, and(iv) local road surface conditions.
 10. The system of claim 5, whereinthe one or more hardware processors are further configured bymachine-readable instructions to: modify, by an alertness detectionmodification component, determination of the alertness level based on atleast one of: (i) a speed of the first vehicle, (ii) traffic conditionsaround the first vehicle, (iii) local weather conditions, and (iv) localroad surface conditions.
 11. A method for verifying whether vehicleoperators are paying attention, the method comprising: generating outputsignals conveying information related to a first vehicle operator;making a first type of determination of at least one of: (i) an objecton which attention of the first vehicle operator is focused duringoperation of a first vehicle, and (ii) a direction in which attention ofthe first vehicle operator is focused during operation of the firstvehicle, wherein the first type of determination is made multiple timesat intervals spanning at least 50 percent of a period of operation ofthe first vehicle, wherein the first type of determination is based onthe generated output signals, and wherein the period spans at least aminute; determining a distraction level of the first vehicle operatorbased on the first type of determination, wherein the distraction levelis represented by a first numerical score; making a second type ofdetermination regarding fatigue of the first vehicle operator duringoperation of the first vehicle, wherein the second type of determinationis made multiple times at particular intervals spanning at least 50percent of the period of operation of the first vehicle, wherein thesecond type of determination is based on the generated output signals;determining a fatigue level of the first vehicle operator based on thesecond type of determination, wherein the fatigue level is representedby a second numerical score; combining the first numerical score withthe second numerical score to determine an aggregated numerical score;making a third type of determination whether the aggregated numericalscore has breached a threshold level; and effectuating a notificationregarding the third type of determination, responsive to determiningthat the aggregated numerical score has breached the threshold level, toat least one of: (i) the first vehicle operator, and (ii) a remotecomputing server.
 12. The method of claim 11, wherein the first vehicleoperator is actively participating in the operation of the first vehicleduring the period of operation.
 13. The method of claim 11, whereinoperation of the first vehicle is actively and primarily controlled byan autonomous driving algorithm.
 14. The method of claim 11, wherein theperiod of operation of the first vehicle spans a trip of the firstvehicle.
 15. The method of claim 11, further comprising: determining aalertness level of the first vehicle operator, wherein the alertnesslevel represents whether the first vehicle operator is paying attentionin a manner that is appropriate for the first vehicle operator being inactive and primary control of the operation of the first vehicle,wherein the alertness level is based on one or more of the first type ofdetermination, the second type of determination, and the third type ofdetermination.
 16. The method of claim 11, further comprising:determining a readiness level of the first vehicle operator, wherein thereadiness level represents whether the first vehicle operator is payingattention in a particular manner that is appropriate for the firstvehicle being actively and primarily controlled by an autonomous drivingalgorithm, wherein the readiness level is based on one or more of thefirst type of determination, the second type of determination, and thethird type of determination.
 17. The method of claim 11, furthercomprising: presenting a challenge to the first vehicle operator,wherein the challenge corresponds to an expected response by the firstvehicle operator, wherein the expected response includes a particularinteraction by the first vehicle operator with a user interface;detecting whether the first vehicle operator provides a response to thechallenge that matches the expected response, wherein the response isprovided through the user interface; wherein one or more of the secondtype of determination and the third type of determination is furtherbased on the detected response to the challenge.
 18. The method of claim17, wherein the notification includes the challenge.
 19. The method ofclaim 11, further comprising: modifying the distraction threshold or thefatigue threshold based on at least one of: (i) a speed of the firstvehicle, (ii) traffic conditions around the first vehicle, (iii) localweather conditions, and (iv) local road surface conditions.
 20. Themethod of claim 15, further comprising: modifying determination of thealertness level based on at least one of: (i) a speed of the firstvehicle, (ii) traffic conditions around the first vehicle, (iii) localweather conditions, and (iv) local road surface conditions.